TY - JOUR
T1 - Spatial patterning among savanna trees in highresolution, spatially extensive data
AU - Staver, A. Carla
AU - Asner, Gregory P.
AU - Rodriguez-Iturbe, Ignacio
AU - Levin, Simon Asher
AU - Smit, Izak P.J.
N1 - Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In savannas, predicting how vegetation varies is a longstanding challenge. Spatial patterning in vegetation may structure that variability, mediated by spatial interactions, including competition and facilitation. Here, we use unique high-resolution, spatially extensive data of tree distributions in an African savanna, derived from airborne Light Detection and Ranging (LiDAR), to examine tree-clustering patterns. We show that tree cluster sizes were governed by power laws over two to three orders of magnitude in spatial scale and that the parameters on their distributions were invariant with respect to underlying environment. Concluding that some universal process governs spatial patterns in tree distributions may be premature. However, we can say that, although the tree layer may look unpredictable locally, at scales relevant to prediction in, e.g., global vegetation models, vegetation is instead strongly structured by regular statistical distributions.
AB - In savannas, predicting how vegetation varies is a longstanding challenge. Spatial patterning in vegetation may structure that variability, mediated by spatial interactions, including competition and facilitation. Here, we use unique high-resolution, spatially extensive data of tree distributions in an African savanna, derived from airborne Light Detection and Ranging (LiDAR), to examine tree-clustering patterns. We show that tree cluster sizes were governed by power laws over two to three orders of magnitude in spatial scale and that the parameters on their distributions were invariant with respect to underlying environment. Concluding that some universal process governs spatial patterns in tree distributions may be premature. However, we can say that, although the tree layer may look unpredictable locally, at scales relevant to prediction in, e.g., global vegetation models, vegetation is instead strongly structured by regular statistical distributions.
KW - Heterogeneity
KW - LiDAR
KW - Savanna
KW - Spatial pattern
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U2 - 10.1073/pnas.1819391116
DO - 10.1073/pnas.1819391116
M3 - Article
C2 - 31085650
AN - SCOPUS:85066452997
SN - 0027-8424
VL - 166
SP - 10681
EP - 10685
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 22
ER -